Elsevier

Atmospheric Environment

Volume 49, March 2012, Pages 1-12
Atmospheric Environment

Review
Critical review of receptor modelling for particulate matter: A case study of India

https://doi.org/10.1016/j.atmosenv.2011.11.060Get rights and content

Abstract

India is used as a case study in reviewing the application of receptor models for source apportionment. India has high concentrations of airborne particulate matter, and the application of effective abatement measures is a high priority, and demands confidence in the results of source apportionment studies. The many studies conducted are reviewed, and reveal a very wide range of conclusions, even for the same city. To some degree these divergences may be the result of using different sampling locations and/or seasons, but to a large extent differences probably arise from methodological weaknesses. The assignment of factors from multivariate receptor models to specific source categories is in many cases highly questionable as factors often include combinations of chemical constituents that are of low plausibility. This ambiguity in terms of presence of tracer elements may be the result of genuine collinearity of diverse sources, or more probably arises from methodological problems. Few studies have used either organic molecular markers or chemical mass balance (CMB) models, and there is a shortage of data on locally-derived emission source profiles, although recent work has begun to remedy this weakness. The conclusions include a number of recommendations for use in design of future studies.

Highlights

► Many studies in India have used multivariate statistical models and a few CMB. ► The results are diverse and not explained entirely by different sampling locations. ► Methodological issues are identified. ► Genuine overlaps in chemical composition between soil and road dust create problems. ► Recommendations are given for future studies.

Introduction

Air quality has been a cause of concern all over the world with the concentrations of criteria pollutants exceeding the standards at many places, particularly in developing countries. Particulate matter (PM) has been recognized as one of the key pollutants with a negative impact on human health, and a range of regulations have been introduced in order to control PM10 levels in urban areas with an increasing focus on PM2.5 control. However, in order to design effective programmes and strategies for reduction of PM concentration in the ambient air, it is necessary to have information about the sources and their respective contributions.

The term, source apportionment (SA) describes techniques used to quantify the contribution of different sources to atmospheric PM concentrations. There is a wide range of published literature on source apportionment using dispersion models and monitoring data (Laupsa et al., 2009; Colvile et al., 2003). However, in the Indian context, most of the source apportionment studies have been conducted using receptor models and hence, receptor models are the focus of this review. Receptor models form a subset of source apportionment techniques and apportion the pollutant concentrations based on the measured ambient air data and the knowledge about composition of the contributing sources (Henry et al., 1984). The key outputs are the percentage contributions of different sources to pollutant concentration. Such models are particularly helpful in cases where complete emissions inventories are not available (Hopke, 1991). Receptor models have been used for identification of sources and their respective contributions to airborne particulate matter across the world (Harrison et al., 1997; Kumar et al., 2001; Larsen and Baker, 2003; Begum et al., 2004; Lai et al., 2005; Song et al., 2006; Tsai and Chen, 2006; Chowdhury et al., 2007; Guo et al., 2009; Kong et al., 2010; Stone et al., 2010; Gu et al., 2011).

Receptor models can be divided into two broad categories: microscopic and chemical. Microscopic methods, including optical, scanning electron microscope (SEM) and automated SEM analyses are primarily based on the analysis of morphological features of many individual particles in the ambient air (Cooper and Watson, 1980). However, they are not very feasible for large-scale use since they do not produce quantitative results in most cases. Chemical methods, on the other hand, utilize the chemical composition of airborne particles for identification and apportionment of sources of PM in the atmosphere. A number of different models are included in this category such as enrichment factor analysis, times series analysis, Chemical Mass Balance (CMB) analysis, multivariate factor analysis (including Principal Component Analysis (PCA) and Positive Matrix Factorization (PMF)), UNMIX, species series analysis and Multilinear Engine (ME) analysis (Cooper and Watson, 1980; Henry et al., 1984; Hopke, 1991; Ramadan et al., 2003). Such methods use trace elements, elemental/organic carbon and organic molecular markers for identification of sources and over time have become popular for SA analyses.

Since PM is composed of both inorganic (trace metals, cations and anions) and organic species, a range of source markers are used in receptor modelling studies. Traditionally, most studies were carried out using inorganic trace elements like Fe, Zn, Pb, Cr, Al and Ni. However, since many of the trace elements are emitted from a range of sources (e.g., Zn is emitted from tyre wear as well refuse burning), it was difficult to apportion the PM to sources with a high degree of confidence. Further, with the removal of elements like Pb and Br from gasoline, there has been a need to develop and use new markers. In the last two decades, research has focused on the identification and development of organic molecular markers for SA since they can be characteristic of sources, thus reducing the source ambiguity, and creating markers for sources which are difficult to be apportioned solely on the basis of inorganic tracers, e.g., levoglucosan for biomass burning (Harrison et al., 1996, 2003; Schauer et al., 1996; Robinson et al., 2006).

The CMB method requires a priori knowledge of the composition of all sources contributing to the airborne pollution, but not their emission rates. The measured air quality is assumed to be a linear sum of the contributions of the known sources, whose contributions are summed over each different sampling period to give the best match to the concentrations of the many chemical species measured in the atmosphere. In more recent studies, organic “molecular markers” which may be only minor constituents of emissions are measured, as these help to discriminate between similar sources (e.g., gasoline and diesel engines).

There is a suite of multivariate statistical methods based upon factor analysis, of which PMF has been developed specifically for the purpose of source apportionment of air quality data, and is the most commonly applied. The method requires no a priori knowledge of source composition, but any information on source emissions characteristics is helpful in discriminating between similar sources. The method requires a substantial number (at least 50) of separate air samples and works best with a large dataset in which the number of samples far exceeds the number of analytical variables. A minimum variable to case ratio of 1:3 should be maintained in order to obtain accurate results (Thurston and Spengler, 1985). For a clearer distinction, it is better to have short sampling times so that overlap of multiple point source contributions to a given sample is minimised. The samples are analysed for the chemical constituents, and those constituents from the same source have the same temporal variation, and if unique to that source are perfectly correlated. Typically, however, a given chemical constituent will have multiple sources and the program is able to view correlations in a multidimensional space and can generate chemical profiles of “factors” with a unique temporal profile characteristic of a source. Past knowledge of source chemical profiles is used to assign factors to sources, and typically identification of six or seven different sources is a good outcome. Before PMF became widely adopted, PCA was widely used for the same purpose, but is less refined than PMF. Input data plays an important role in the final results, and care has to be taken to ensure that this is of good quality and where possible uncertainties can be assigned to individual analytes.

The key differences between CMB and the methods based upon multivariate statistics are summarised in Table 1. Studies have been conducted to compare results from different models (Larsen and Baker, 2003; Ramadan et al., 2003; Shrivastava et al., 2007; Bullock et al., 2008; Lee et al., 2008; Viana et al., 2008b; Yatkin and Bayram, 2008; Callén et al., 2009; Tauler et al., 2009). Multicollinearity can affect the model estimates, particularly in cases where different sources have similar signatures, although multivariate models help to reduce that problem substantially (Henry et al., 1984; Thurston and Lioy, 1987). It has been reported that in cases where two different sources have similar signatures, it becomes difficult to distinguish between them and neither CMB nor multivariate models can distinguish between sources with similar signatures when additional information (for e.g., meteorology data) is missing (Henry et al., 1984).

Hybrid models such as target transformation factor analysis (TTFA) and the constrained physical receptor model (COPREM) have been designed to combine the features of CMB and factor analysis models with the aim of maximizing the advantages while minimizing the limitations of each model (Wahlin, 2003; Viana et al., 2008a). The Multilinear Engine (ME) program also allows the use of source composition data to constrain the model.

Larsen and Baker (2003) compared three different multivariate techniques- UNMIX, PCA/MLR and PMF for SA of ambient polyaromatic hydrocarbons (PAHs) in Baltimore. Although they reported that PCA/MLR is unable to model extreme data effectively, they concluded that the overall source contributions compare well among the various models. They also reported that use of different techniques on the same data set could help in identification of missing sources, and increase the robustness of the results. Shrivastava et al. (2007) used PMF and CMB for source apportionment of organic carbon and found good correlation between individual profiles for CMB and factors identified by PMF but with systematic biases that were found to be within an acceptable range (a factor of two). Lee et al. (2008) compared the CMB and the PMF models and concluded that although both models identify similar sources, they apportion contributions of different sources differently. The authors suggested that a lack of local source profiles, omission of key sources or lack of suitable markers, and the different assumptions regarding aging of the source emissions as the possible causes for the different estimations. Viana et al. (2008b) compared PCA, CMB and PMF for identification of source contributions to PM10 in Spain. They reported overall consistency between the different models with high correlation in terms of source identification. However, they noted larger differences in terms of the percentage contribution of various sources. They suggested that a combined approach with the use of multivariate techniques for identification and interpretation of emissions sources and use of CMB for source contribution could help in increasing the robustness of the results. Earlier, Thurston and Lioy (1987) had also suggested a similar approach with the consecutive use of multivariate and chemical mass balance models to derive better results from receptor modelling studies. Similarly, Shi et al. (2011) tested a combined two-stage PCA/MLR- CMB model and found acceptable results using synthetic datasets with collinearity. They also concluded that maximum uncertainty is generally observed in case of highly collinear sources.

Callén et al. (2009) compared three different multivariate techniques- PCA-ACPS, UNMIX, PMF for source apportionment of PM10 and found that the different models showed high correlation between modelled and measured concentrations and PCA and PMF were able to identify more sources in comparison with UNMIX with good agreement. Tauler et al. (2009) compared four different multivariate models (PCA, PMF, Multivariate Curve Resolution by Alternating Least Squares, (MCR-ALS) and Weighted Alternating Least Squares (MCR-WALS)) and concluded that PMF and MCR-WALS identify sources and apportion the emissions to sources in a similar fashion. The weighted models (PMF and MRC-WALS) were found superior in robust and accurate factor identification.

Receptor models have been used for regulatory purposes since they were first used in Oregon, USA in the late 1970s (Gordon, 1988). However, there is a caveat regarding the degree of uncertainty associated with the results (Caselli et al., 2006).

Given the rapid rates of urbanization in Indian cities, air pollution is increasingly becoming a critical threat to the environment and to the quality of life among the urban population in India. Air quality has been a cause of concern in Indian cities with the concentrations of criteria pollutants exceeding health-based standards, and PM has been identified as one of the key public health concerns. High enrichment factors have been reported for various metals including Pb, Zn, Cu, Ni and Cr in a number of Indian cities, indicative of anthropogenic sources of heavy metals in particulate matter (Kulshrestha et al., 1995; Pandey et al., 1998; Negi et al., 2002; Rastogi and Sarin, 2009). Also, using SEM–EDX analysis, Srivastava et al. (2009b) reported that particles were primarily of anthropogenic origin irrespective of size range in polluted areas, e.g., traffic intersections. Although there has been an increased focus on PM emission control in recent years, the concentrations are still found to exceed the National Ambient Air Quality Standards (NAAQS) regularly.

The primary sources of air pollution in India have been identified as vehicular emissions, industrial emissions, coal combustion, biomass burning, road dust and refuse burning. There has been a rapid increase in motorization in India in the past years and this has led to an increasing contribution of the transport sector to air pollution in urban areas. Small-scale industries functioning within urban centres have been found to contribute to the air pollution problem.

Section snippets

Source apportionment and receptor modelling in India

There has not to our knowledge previously been a review of either aerosol source apportionment or receptor modelling work conducted in India. In this article, we seek to review existing knowledge and to make recommendations as to future directions. There is a growing body of literature on source apportionment of PM in India using receptor modelling (Table S1 in Supplementary Information). A majority of the SA studies have been conducted using multivariate methods; PCA being the most commonly

Discussion and conclusions

There have been many studies conducted in India using receptor modelling methods for source apportionment of particulate matter. India is a very large and diverse country, and unsurprisingly the studies have drawn widely differing conclusions. Even within individual Indian cities, different authors have come to widely varying conclusions over source attribution and apportionment, and this may to some extent be a result of using different sampling locations and seasons. Most studies have

Acknowledgement

Pallavi Pant gratefully acknowledges financial support from the University of Birmingham fund for collaboration with India.

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